YAPAY ZEKÂ VE FİNANSAL RİSK YÖNETİMİNDE YENİLİKLER

Abstract

Bu çalışma, yapay zekâ temelli yöntemlerin finansal risk yönetiminde yarattığı dönüşümü kavramsal bir çerçevede incelemektedir. Geleneksel risk yönetimi yaklaşımlarının doğrusal varsayımlara ve sınırlı veri işleme kapasitesine dayanması, finansal piyasaların artan karmaşıklığı karşısında yetersiz kalmaktadır. Bu bağlamda makale, makine öğrenmesi, derin öğrenme ve doğal dil işleme uygulamalarının kredi, piyasa, likidite ve operasyonel risklerin yönetiminde nasıl konumlandığını ve mevcut risk yönetimi mimarisini hangi açılardan dönüştürdüğünü ele almaktadır. Çalışma, ekonometrik analiz içermeyen, güncel literatürün sistematik değerlendirilmesine dayalı nitel bir yöntem benimsemektedir. Bulgular, yapay zekâ tabanlı modellerin tahmin gücünü artırdığını, gerçek zamanlı veri kullanımını mümkün kıldığını ve doğrusal olmayan risk dinamiklerini yakalayabildiğini göstermektedir. Bununla birlikte, model şeffaflığı, açıklanabilirlik ve algoritmik önyargı gibi yeni risk alanları da ortaya çıkmaktadır. Bu doğrultuda çalışma, yapay zekânın etkin ve sürdürülebilir kullanımı için yönetişim ve düzenleyici uyumun güçlendirilmesine yönelik politika çıkarımları sunmaktadır.

Keywords

• Yapay zekâ; finansal risk yönetimi
• makine öğrenmesi
• sistemik risk
• algoritmik karar alma
• finansal istikrar
• model riski
• regülasyon ve denetim

Supporting Institution

Bu çalışmanın hazırlanma sürecinde herhangi bir kurum, kuruluş veya kişi tarafından maddi ya da akademik destek alınmamıştır.

Ethical Statement

Yazar herhangi bir çıkar çatışması olmadığını beyan etmektedir.

Thanks

Yazar, çalışmanın değerlendirme sürecinde katkı sunan editör ve hakemlere teşekkür eder.

INNOVATIONS IN ARTIFICIAL INTELLIGENCE AND FINANCIAL RISK MANAGEMENT

Abstract

This study examines the transformation of financial risk management driven by artificial intelligence–based methods within a conceptual framework. Traditional risk management approaches, built on linear assumptions and limited data-processing capacity, have become inadequate amid the growing complexity of financial markets. Accordingly, the article analyzes the role of machine learning, deep learning, and natural language processing techniques in managing credit, market, liquidity, and operational risks, and their impact on existing risk management frameworks. The study adopts a qualitative methodology based on a systematic review of recent academic literature, without employing econometric analysis. The findings indicate that AI-based models improve predictive accuracy, enable real-time data use, and capture nonlinear risk dynamics. However, they also introduce challenges related to model transparency, explainability, and algorithmic bias. Therefore, the study highlights the importance of strengthening governance mechanisms and regulatory alignment to ensure the effective and sustainable use of artificial intelligence in financial risk management.

Keywords

• Artificial intelligence
• financial risk Management
• machine learning
• systemic risk
• algorithmic decision making
• financial stability
• model risk
• regulation
• supervision

Supporting Institution

No financial or academic support was received from any institution, organization, or individual during the preparation of this study.

Ethical Statement

The author declares no conflict of interest.

Thanks

The author thanks the editor and reviewers for their valuable contributions during the evaluation process of the study.

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